|Year : 2022 | Volume
| Issue : 2 | Page : 62-69
The effect of physical exposures and job stress on sleep quality and mental health in a group of pink-collar workers in Iran
Mahin Hosseininejad, Shahrbanoo Moslemi, Saber Mohammadi
Occupational Medicine Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
|Date of Submission||06-Oct-2020|
|Date of Decision||30-Mar-2021|
|Date of Acceptance||05-Nov-2021|
|Date of Web Publication||4-Jul-2022|
Dr. Mahin Hosseininejad
Occupational Medicine Department, Faculty of Medicine, Iran University of Medical Sciences, Tehran
Source of Support: None, Conflict of Interest: None
Background: Pink-collar workers are a group of workers in the service industries. Teachers are classified as a group of pink-collar workers, who are under a high level of stress. This study aimed to investigate the effect of physical exposures and job stress on mental health and sleep quality of technical and vocational teachers. Materials and Methods: This cross-sectional study was conducted on 622 teachers. The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) were employed to evaluate sleep status; the Osipow Questionnaire was used to assess job stress; the musculoskeletal intervention center – Norrtalje questionnaire (MUSIC) was used to measure physical exposures; and the 12-Item General Health Questionnaire was used to assess mental health. Results: The mean scores of general health, job stress, and work hardness were 10.97 ± 6.29, 153.40 ± 22.63, and 15.61 ± 2.77, respectively; the mean score of ESS and PSQI were 6.22 ± 3.61 and 5.44 ± 2.97, respectively. The mental health status of the participants was significantly worse with more exposure to various types of job stressors and physical exposures. There was a significant relationship between sleep quality and general health score. Conclusion: The mental health status was considerably better in women, smokers, and people who exercised, have less work experience, do not do shift work, work fewer hours per week, and have good sleep quality. Physical exposures and various occupational stressors can reduce mental health. There was a significant relationship between job stress and decreased sleep quality but sleep quality was not significantly associated with age, BMI, work experience, and working hours per week.
Keywords: General health, job stress, physical exposure, pink-collar worker, sleep quality
|How to cite this article:|
Hosseininejad M, Moslemi S, Mohammadi S. The effect of physical exposures and job stress on sleep quality and mental health in a group of pink-collar workers in Iran. Indian J Occup Environ Med 2022;26:62-9
|How to cite this URL:|
Hosseininejad M, Moslemi S, Mohammadi S. The effect of physical exposures and job stress on sleep quality and mental health in a group of pink-collar workers in Iran. Indian J Occup Environ Med [serial online] 2022 [cited 2022 Aug 16];26:62-9. Available from: https://www.ijoem.com/text.asp?2022/26/2/62/349860
| Introduction|| |
There are many different jobs in the world, which are typically classified as white-collar, blue-collar, pink-collar, or green-collar jobs, etc. Every job has its characteristics based on its requirements and responsibilities.,
Pink-collar workers are a group of workers in service industries, as waiters, secretaries, and salespersons. The term pink-collar was first used after World War II to describe occupations traditionally done by women, including clerical jobs, secretarial jobs, and assistance. In today's world, these jobs are no longer exclusive to women and entail a wider range of service jobs. In general, people performing pink-collar jobs are working as assistants, and in customer service, entertainment, sales, and management sectors.
Given the expanding range of these jobs, considering their various aspects with an effect on people who perform those jobs seems essential.
Teaching is classified as a pink-collar job. In the past few years, mental health problems have become a growing challenge facing teachers in many countries. Mental disorders are one of the major reasons for early retirement among teachers. As teachers have a vital responsibility to educate and prepare future generations to live and work in an ever-evolving and more complicated world, their health is an interdisciplinary issue and is important from different sociopolitical aspects. Many studies have shown that teaching exposes teachers to highly demanding situations, and therefore it is classified as a highly stressful job. Since few studies have compared teaching with other occupations, the workload may not be well interpreted and other aspects of the job, such as the diversity of tasks, be neglected. The majority of studies on teachers have only considered psychological stress in the workplace and rarely addressed physical exposures.
Given the vital role of teachers and the special importance of identifying their general health and factors affecting it for implementing prevention programs to reduce complications and costs, the need for further studies is felt.
In the present study, a group of technical and vocational teachers was examined. Some skills trained by these teachers include welding, turning, milling, handcrafting (design and sewing, pottery, shoes and bag making, carpet weaving, etc.), food industry, woodcutting, fish farming, mushroom and saffron production, agriculture, animal husbandry, horticulture, textile, mechanics, cabinetry, information technology, tourism, electronics, cooking, hairdressing, driving, and photography.
The main purpose of this study was to investigate the effect of occupational factors, physical exposures (including sustained sitting work, work in front of a video display terminal, vibrating surface, vibrating tools, precise and sensitive work, hand above the shoulder, hand below the knee, bending and twisting, repetitive hand movement, and heavy lifting), and job stress (including work overload, role ambiguity, role insufficiency, role conflict, and responsibility) on mental health and sleep quality (including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleeping medication, and daytime dysfunction) of teachers.
| Materials and Methods|| |
This cross-sectional study was conducted among the teachers of the Technical and Vocational Organization of Alborz Province in Iran. Among the 800 teachers who were eligible to participate in the study, 622 answered the questions. First, necessary arrangements were made with relevant authorities for collecting and recording the required information. Then, the participants were given the necessary explanations about the process and how they can participate in the study. To collect data, a semistructured questionnaire was used that included demographic information of the participants, such as age, gender, height, weight, marital status, number of children, level of education, smoking status, work experience, working hours per week, and work shift. In this study, the participants were divided into the morning shift and evening shift groups. The inclusion criteria were all consenting teachers with at least 1 year's work experience. The exclusion criteria included teachers with a history of approved psychological disorder, other disorders that may affect the sleep quality such as preexisting heart disease (e.g., heart failure) or lung disease (e.g., COPD), as well as sleep disorders with other causes such as obstructive sleep apnea, etc., and medications use or an irrelevant second job. Participants were divided into two groups based on the mean of age (<37 years and ≥37 years) and the average work experience (<11 years and ≥11 years). Besides, subjects were divided into single and married groups based on their marital status, and the singles group included people who were not married, divorced, or widowed, or living apart from their spouse for any reason.
All participants signed informed written consent forms, and the study was approved by the Ethics Committee of Iran University of Medical Sciences (Code: IR.IUMS.REC.1397.766).
Data Collection Instruments
Pittsburgh sleep quality index questionnaire (PSQI)
The PSQI was used to analyze sleep quality. The questionnaire entails 19 items, used to create seven components with a score ranging between 0 (no problem) and 3 (major problem). The total score from these seven components varies between 0 (no problem) and 21 (major problem). Based on the previous studies, a global score of ≥5 could be used to identify people with poor sleep quality., So the cutoff point in this study was considered 5. People with a score of 5 or higher, experienced poor sleep quality, and those with a score of less than 5 experienced good sleep quality. The validity and reliability of the questionnaire were approved by Moghaddam et al. with a Cronbach's alpha of 0.77.
Epworth Sleepiness Scale (ESS)
The ESS was employed to measure excessive daytime sleepiness. This 8-item questionnaire measures a person's general level of sleepiness and its likelihood in different situations. Each item is scored between 0 and 3. The overall ESS score ranges between 0 (no chance of sleepiness) to 24 (high chance of sleepiness in all 8 conditions). Clinically, an ESS score higher than 10 indicates excessive daytime sleepiness. A domestic study confirmed the validity and reliability versions of ESS.
Osipow job stress questionnaire
Osipow's questionnaire was used to analyze job stress. It was scored on a 5-point Likert scale anchored by never, occasionally, often, usually, and most of the time. The questionnaire is designed based on the occupational role and divided into six subgroups. Based on the scores obtained, the effect of each stressor is divided into four categories: low (10 to 19 points), low to moderate (20 to 29), moderate to severe (30 to 39), and severe (40 to 50). The overall stress level is similarly divided into four categories, namely, low (50 to 99), low to moderate (100 to 149), moderate to severe (150 to 199), and severe (200 to 250). Sharifian et al. measured the validity and reliability of the Farsi version of this questionnaire and obtained the Cronbach's alpha of 0.89.
Musculoskeletal intervention center – Norrtalje questionnaire (MUSIC)
To evaluate physical exposure, the MUSIC inventory was employed. The first question is a visual analog scale that measures work hardness and is scored between 6 and 20. The subsequent questions about the types of physical exposures were scored on a 5-point Likert scale anchored by never, 1/4 of the times, 1/2 of the times, 3/4 of the times, and always. In this study, “never” and “1/4 of the times” indicate low exposure, and “1/2 of the times,” “3/4 of the times,” and “always” indicated high levels of exposure. A domestic study confirmed the validity and reliability of its Persian version in measuring musculoskeletal pain and disorders and work-related physical exposures.
12-item general health questionnaire
To measure mental health status, the 12-item general health questionnaire measures both positive (6 items) and negative (6 items) aspects of mental health. In this questionnaire, the Likert scale was used for data analysis. The first scoring format of this questionnaire is 0-1-2-3. A higher total score indicates problems with mental health. The second scoring format is 0-0-1-1, based on which a GHQ score higher than 2 indicates psychological distress, and scores higher than 4 indicate psychological impairment. In other words, participants with an overall score of 4 or higher have a major mental disorder. Based on a study conducted in Iran in 2003, it was found that the Persian version of this questionnaire is a valid and reliable tool for assessing mental health status (Cronbach's alpha = 0.87).
After excluding the uncompleted questionnaires, the remaining questionnaire was analyzed with SPSS 22. The quantitative variables were expressed as mean and standard deviation and the qualitative variables as frequency and percentage. The independent t-test was used to examine the relationship between mental health status with demographic and occupational factors as well as physical exposure and for comparison of general health scores, sleep quality, daytime sleepiness, and job stress in males and females. Analysis of variance (ANOVA) was used to assess mental health status, daytime sleepiness, and sleep quality based on whole job stress and its items. A correlation test was used to assess the relationship between sleep quality, job stress, and general health. Regression analysis was performed to further investigate the relationship between mental health and physical exposure by eliminating the effect of contextual variables. The P value <0.05 was considered to be significant in this study.
| Results|| |
Of 800 questionnaires distributed among participants, 622 were completed with a response rate of 75%.
In the present study, 38.3% (n = 238) of the participants were females and 61.7% (n = 384) were males.
Of the participants, 4.3% (n = 27) were smokers with 5.22 pack-years on average. Moreover, 18.2% of the participants exercised regularly and 56.8% irregularly. The average working hours per week among participants was 39 h.
Data on mental health status
The mean general health score in the participants was 10.97 ± 6.29 with the lowest and highest scores of 0 and 33, respectively. In addition, 28% (n = 174) of the participants experienced significant mental disorders (score ≥4). The t-test results showed that mental health was significantly better in women, smokers, people who exercised, those with less work experience, none shift workers (morning shift workers), and working hours fewer than 40 h per week [Table 1].
|Table 1: Assessing mental health status based on demographic and occupational factors by independent t-test|
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Data on physical exposures
The mean score of work hardness was 15.61 ± 2.77 with the lowest score of 6 and the highest score of 20. Most participants (22%) reported a score of 15 for work hardness.
The mean score of work hardness was 15.23 ± 2.86 in women and 15.85 ± 2.68 in men, indicating a significant between-group difference (P-value: 0.007). In addition, sedentary work, working with a computer, and frequent hand movements were more frequent in women, whereas exposure to whole-body or hand-arm vibration, doing precise and sensitive work for more than 2 h, hands doing below-knee work, frequent bending and twisting, performing above-shoulder work, and heavy lifting were more frequent in men.
On the relationship between general health and physical exposures, the GHQ score was higher (indicating poorer mental health status) in participants with high exposure, except in sedentary work [Table 2].
|Table 2: Comparison of general health scores based on physical exposures by independent t-test|
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In addition, by performing regression analysis for further investigation, considering the variables that were significantly related to mental health status in the initial analysis, a significant relationship was observed between poor mental health and working with a computer (P-value = 0.045, OR = 1.54) and bending and twisting (P-value = 0.021, OR = 1.65). Besides, among the demographic and occupational factors, the relationship between mental health status with exercise (P-value <0.001, OR = 2.93) and weekly work hours (P-value = 0.007, OR = 1.92) was still significant. In other words, the mental health status was considerably better for participants who exercised and work fewer hours per week [Table 3].
|Table 3: Regression analysis of the relationship between physical exposure and general health status by eliminating the effect of contextual variables|
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Data on daytime sleepiness and sleep quality
The mean score of ESS was 6.22 ± 3.61 with the lowest and highest scores of 0 and 24, respectively. Seventy-nine participants (12.7%) had abnormal daytime sleepiness (score >10). People with abnormal ESS scores were older (39 vs. 36 years old; P value <0.001) with more work experience (14 vs. 12 years; P value: 0.002). In addition, the mental health status was significantly worse in the participants with abnormal ESS scores (15.35 vs. 10.33; P value <0.001). The relation of ESS score with working hours per week and total stress level was insignificant.
The mean PSQI was 5.44 ± 2.97 with the lowest and highest scores of 0 and 18, respectively. According to the PSQI, 344 participants (55.3%) had lower sleep quality (score ≥5). Descriptive statistics for each of the Pittsburgh questionnaire components are shown in [Supplementary Table 1].
There was a significant relationship between sleep quality and general health score, given that the general health score was 8.69 ± 5.33 in participants with good sleep quality and 12.82 ± 6.41 in those with poor sleep quality (P < 0.001). In other words, poor sleep quality was associated with a lower level of mental health. There was no significant relationship between sleep quality with age, BMI, work experience, and working hours per week.
Data on job stress
In the study of job stress based on the Osipow questionnaire, it was found that most job stressors were in the mild to moderate category. The role ambiguity and role insufficiency were categorized in the moderate to severe category. The mean overall score of job stress was 153.40 ± 22.63 and participants were categorized in the moderate to severe category in terms of total stress. There was no significant relationship between total stress and age, working hours per week, and work experience. The analysis of the relationship between job stress and mental health status showed that almost in all cases, the general health level significantly reduced with increasing stressors.
There was a significant relationship between sleepiness and all stressors (except the role ambiguity) and total stress. Investigating the relationship between sleep quality and job stress, sleep quality decreased significantly with increasing stress levels in the case of work overload, role conflict, responsibility, and whole stress [Table 4]. Post hoc analysis is shown in [Supplementary Table 2]. Based on the post hoc analysis, most of the differences between groups were observed in the relationship between mental health, daytime sleepiness, and sleep quality with work overload. The next most significant differences between groups were observed in the relationship between mental health and daytime sleepiness with whole job stress.
|Table 4: Comparison of General Health score, Epworth Sleepiness Scale, and Pittsburgh Sleep Quality Index between different levels of job stressors by analysis of variance (ANOVA)|
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Examining the relationship between sleep quality, job stress, and mental health using correlation, it was found that there is a significant relationship between sleep quality and mental health (P-value <0.001; correlation coefficient: 0.443). There was also a weaker relationship between job stress and mental health (P-value = 0.001; correlation coefficient = 0.127) as well as job stress and sleep quality (P-value = 0.020; correlation coefficient = 0.093). [Figure 1] separately shows the relationship between job stress, sleep quality, and general health.
|Figure 1: Scatter plot for relationship between sleep quality, job stress and general health|
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To provide a general view of the study population, different variables in both men and women are compared in [Table 5]. This table shows no significant between-gender difference in sleep quality, daytime sleepiness, and total job stress. However, mental health was significantly poorer in men than in women.
|Table 5: Comparison of general health scores, sleep quality, daytime sleepiness, and job stress in both gender by independent t-test|
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[Table 6] provides the concomitant effect of job stress and physical exposures on mental health status. Participants were divided into high and low groups in terms of physical exposure and job stress levels and the mean score of the general health questionnaire in each group was compared with the other group using the independent t-test. As shown in this table, the concurrent exposure to stress and physical factors has no significant effect on mental health (except in a combined effect of high stress and above-shoulder work).
|Table 6: The effect of concurrent exposure to job stress and physical factors on general health by comparison of the general health score in high and low exposure groups in terms of physical exposure and job stress levels|
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| Discussion|| |
The present study aimed to investigate the effect of physical exposures and work-related factors on sleep quality and mental health of 622 teachers. The mean score of general health was 10.97 ± 6.29, and 28% of the participants indicated significant psychological impairment, which was consistent with other relevant studies on teachers., Based on gender, 24.4% of women and 30.2% of men experienced a lower level of mental health. In this regard, some studies were consistent with the present study. In contrast, Van Droogenbroeck and Spruyt did not find any significant between-gender differences in psychological problems. In a study conducted by Bannai et al. on Japanese teachers, psychological distress was higher in women (57.8%) than men (47.8%). This difference between studies can be due to the cultural differences found in different communities. In addition, in our study, men were older than women and had more work experience and working hours per week, which may explain more cases of mental health problems among men.
In the present study, there was no significance between gender difference in sleep quality, daytime sleepiness, and total job stress. Various studies in this field produced conflicting results. In some studies, the level of stress in female teachers was higher than in male teachers,; whereas, some other studies reported a higher level of stress in male teachers. Consistent with the present study, Samad et al. did not find any significant between-gender differences in the level of stress. No significant age-related difference was observed in the mean mental health score. Bernotaite et al. reported that psychological distress increased with age but did not differ according to age groups. In the study by Bauer et al. and Seibt et al., there was no significant difference between age and general health.
In the present study, the participants with more working hours per week had a higher score in mental health (i.e., lower mental health level), which could be due to their heavier workload. In a study by Seibt et al. on the predictors of mental health in female teachers, the participants with mental health problems (GHQ12 ≥5) worked more hours per week. This finding can guide employers to improve workers' mental health by modifying work schedules and thus reducing costs and losses.
In our study, smokers experienced a higher level of general health, which is difficult to confirm given the low number of smokers among the participants (n = 27). Seibt et al. did not observe any difference in the incidence of mental health problems based on smoking status. In addition, due to the lower smoking prevalence among teachers, this variable has been investigated in a few studies and thus more relevant studies are required.
In mental health, the level was significantly higher in participants who exercised. In the study conducted by Austin et al., doing physical exercise was reported as an effective coping strategy to reduce the level of stress. Seibt et al. showed that those teachers who did not exercise accounted for the majority in the poor health group (28.3% vs. 26%) and those who exercised once a week were in the high general health group (30.8% vs. 28.3%). Based on this finding, it can be concluded that encouraging the workforce to exercise regularly along with providing appropriate conditions in the work environment for regular exercise can have a beneficial effect on mental health and reduce job stress among workers.
The mental health level in the participants with poor sleep quality was significantly lower than those with good sleep quality. In a study conducted by Valerio et al. on the association between stress, general health, and alcohol consumption with poor sleep quality among university students, it was shown that the level of sleep quality reduced with reducing general health level.
The initial analysis to examine the relationship between physical exposures and general health status showed that in all cases except sedentary work, participants with more exposure experienced a significantly lower level of mental health. Based on the regression analysis, this relationship was still significant for working with computers and bending and twisting. Few studies have been conducted on the relationship between physical exposures and general health in teachers. In a study by Parkes, it was shown that physical stressors at the workplace can predict health outcomes; however, no significant relation was found with general health status. In addition, Salin et al. showed that the physical conditions of the workplace play a role in job burnout.
Results of the present study indicated that there is an inverse statistical correlation between various stressors and mental health status and employees with a high level of job stress experience lower mental health status. A few studies to investigate the relationship between job stress and general health status provided results consistent with the present study. For example, Lim et al. investigated accounting students and observed a significant relationship between their general health status and degree of stress. Studies on bank workers and office workers showed a significant inverse relationship between different dimensions of stress and general health. Therefore, considering the negative impact of job stress on mental health status, it is necessary to pay attention to workplace stressors and find solutions to reduce them so that they can improve the mental health of the workforce and lead to positive outcomes for employees and employers.
In the present study, the effect of concomitant exposure to job stress and physical exposures on general health status was examined and no significant relationship was found (except in a combined effect of high stress and above-shoulder work).
Examining the relation between job stress and sleep quality, it was found that job stress is directly correlated with poor sleep quality. Various studies have been conducted on the relation between job stress and sleep quality. For instance, a study by Kottwitz et al. on the effect of job stress on sleep quality in teachers showed that their sleep quality improved during holidays.
In this study, the effect of stress and physical exposures on mental health status and sleep quality of teachers was investigated simultaneously. But that strength has led to some limitations. Using several questionnaires could reduce the participants' accuracy in answering all the questions. To overcome this limitation, a trained person has full supervision over answering the questionnaires.
| Conclusion|| |
Based on the findings of this study, the mental health status was better in women and smokers. In addition, exercise improved mental health. On the other hand, the mental health level was lower in people with high work experience, more working hours per week, shift workers, and people with lower sleep quality. Physical exposures at the workplace, especially working with a computer and bending and twisting, and encountering all kinds of job stressors result in increased mental health scores (worse mental health status). However, no effect was found between concomitant exposure to job stress and physical exposures on general health status. There was a significant relationship between sleep quality and mental health score in that poor sleep quality was associated with a lower level of mental health. There was no significant relationship between sleep quality with age, BMI, work experience, and working hours per week.
This study was conducted with support from the deputy for research at the Iran University of Medical Sciences.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Gupta N. Perceived Quality of Work-Life in Relation to Collar Jobs: Blue-Collar & Pink_Collar Workers. International Journal of Research and Analytical Reviews 2019; 6:415-22.
Labbafinejad Y. Influence of short term and long-term sickness absence on work ability index. Iran Occup Health 2019;16:1-12.
Hodson R, Sullivan TA. The Social Organization of Work.Wadsworth, Ohio: Cengage Learning; 2012.
Basu S, Ratcliffe G, Green M. Health and pink-collar work. Occup Med 2015;65:529-34.
Koopmans L, Bernaards CM, Hildebrandt VH, Van Buuren S, Van der Beek AJ, De Vet HC. Improving the individual work performance questionnaire using rasch analysis. J Appl Meas 2014;15:160-75.
Bauer J, Stamm A, Virnich K, Wissing K, Müller U, Wirsching M, et al
. Correlation between burnout syndrome and psychological and psychosomatic symptoms among teachers. Int Arch Occup Environ Health 2006;79:199-204.
Bauer J, Unterbrink T, Hack A, Pfeifer R, Buhl-Grießhaber V, Müller U, et al
. Working conditions, adverse events and mental health problems in a sample of 949 German teachers. Int Arch Occup Environ Health 2007;80:442-9.
Seibt R, Matz A, Hegewald J, Spitzer S. Working conditions of female part-time and full-time teachers in relation to health status. Int Arch Occupat Environ Health 2012;85:675-87.
Bellingrath S, Weigl T, Kudielka BM. Chronic work stress and exhaustion is associated with higher allostastic load in female school teachers. Stress 2009;12:37-48.
Rana A, Soodan V. Effect of occupational and personal stress on job satisfaction, burnout, and health: A cross-sectional analysis of college teachers in Punjab, India. Indian J Occup Environ Med 2019;23:133-40.
] [Full text]
Maheshwari G, Shaukat F. Impact of poor sleep quality on the academic performance of medical students. Cureus 2019;11:e4357.
Tsai P, Wang S, Wang M, Su C, Yang T, Huang C, et al
. Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res 2005;14:1943-52.
Moghaddam JF, Nakhaee N, Sheibani V, Garrusi B, Amirkafi A. Reliability and validity of the Persian version of the Pittsburgh Sleep Quality Index (PSQI-P). Sleep Breath 2012;16:79-82.
Dehghani M, Sadeghniat K, Ebrahimi M. Relationship between Epworth Sleepiness Scale and obstructive sleep apnea with road accidents. Iran Occup Health 2015;12:87-96.
Haghighi KS, Montazeri A, Mehrizi AK, Aminian O, Golkhandan AR, Saraei M, et al
. The Epworth Sleepiness Scale: Translation and validation study of the Iranian version. Sleep Breath 2013;17:419-26.
Sharifian S, Aminian O, Kiyani M, Barouni S, Amiri F. The evaluation of the degree of occupational stress and factors influencing it in forensic physicians working in legal medicine organization in Tehran-autumn of 2005. Scientific Journal of Forensic Medicine 2006; 12:144-50.
Alipour A, Ghaffari M, Jensen I, Shariati B, Vingard E. Reliability and validity study of Persian modified version of MUSIC (musculoskeletal intervention center)–Norrtalje questionnaire. BMC Musculoskelet Disord 2007;8:88.
Van Droogenbroeck F, Spruyt B. Do teachers have worse mental health? Review of the existing comparative research and results from the Belgian Health Interview Survey. Teach Teach Educ 2015;51:88-100.
Montazeri A, Harirchi AM, Shariati M, Garmaroudi G, Ebadi M, Fateh A. The 12-item General Health Questionnaire (GHQ-12): Translation and validation study of the Iranian version. Health Qual Life Outcomes 2003;1:66.
Bernotaite L, Malinauskiene V. Workplace bullying and mental health among teachers in relation to psychosocial job characteristics and burnout. Int J Occup Med Environ Health 2017;30:629-40.
Bannai A, Ukawa S, Tamakoshi A. Long working hours and psychological distress among school teachers in Japan. Journal of occupational health. 2015;57:20-7.
Chaplain RP. Stress and psychological distress among trainee secondary teachers in England. Educ Psychol 2008;28:195-209.
Yang X, Ge C, Hu B, Chi T, Wang L. Relationship between quality of life and occupational stress among teachers. Public Health 2009;123:750-5.
Kovess-Masféty V, Sevilla-Dedieu C, Rios-Seidel C, Nerrière E, Chee CC. Do teachers have more health problems? Results from a French cross-sectional survey. BMC Public Health 2006;6:1-13.
Samad NIA, Hashim Z, Moin S, Abdullah H. Assessment of stress and its risk factors among primary school teachers in the Klang Valley, Malaysia. Glob J Health Sci 2010;2:163-71.
Seibt R, Spitzer S, Druschke D, Scheuch K, Hinz A. Predictors of mental health in female teachers. Int J Occup Med Environ Health 2013;26:856-69.
Austin V, Shah S, Muncer S. Teacher stress and coping strategies used to reduce stress. Occup Ther Int 2005;12:63-80.
Valerio TD, Kim MJ, Sexton-Radek K. Association of stress, general health, and alcohol use with poor sleep quality among US college students. Am J Health Educ 2016;47:17-23.
Parkes KR. Shiftwork, job type, and the work environment as joint predictors of health-related outcomes. J Occup Health Psychol 1999;4:256-68.
Salin D. Risk factors of workplace bullying for men and women: The role of the psychosocial and physical work environment. Scand J Psychol 2015;56:69-77.
Lim Y-M, Tam C-L, Lee T-H. Perceived stress, coping strategy and general health: A study on accounting students in Malaysia. Researchers World 2013;4:88-95.
Guidi S, Bagnara S, Fichera G. The HSE indicator tool, psychological distress and work ability. Occup Med 2012;62:203-9.
Mohammadi S, Hosseininejad M, Taghizadeh S, Kabir-Mokamelkhah E. The relationship between different aspects of occupational stress and general health. Hygiene and Sanitation 2020; 99:947-50.
Kottwitz MU, Gerhardt C, Pereira D, Iseli L, Elfering A. Teacher's sleep quality: Linked to social job characteristics? Ind Health 2017;56:53-61.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]